PyUNIxMD: A Python-based excited state molecular dynamics package.
Ontology highlight
ABSTRACT:
SUBMITTER: Lee IS
PROVIDER: S-EPMC8362049 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Many biological processes are regulated by allosteric mechanisms that communicate with distant sites in the protein responsible for functionality. The binding of a small molecule at an allosteric site typically induces conformational changes that propagate through the protein along allosteric pathways regulating enzymatic activity. Elucidating those communication pathways from allosteric sites to orthosteric sites is, therefore, essential to gain insights into biochemical processes. Targeting the allosteric pathways by mutagenesis can allow the engineering of proteins with desired functions. Furthermore, binding small molecule modulators along the allosteric pathways is a viable approach to target reactions using allosteric inhibitors/activators with temporal and spatial selectivity. Methods based on network theory can elucidate protein communication networks through the analysis of pairwise correlations observed in molecular dynamics (MD) simulations using molecular descriptors that serve as proxies for allosteric information. Typically, single atomic descriptors such as α-carbon displacements are used as proxies for allosteric information. Therefore, allosteric networks are based on correlations revealed by that descriptor. Here, we introduce a Python software package that provides a comprehensive toolkit for studying allostery from MD simulations of biochemical systems. MDiGest offers the ability to describe protein dynamics by combining different approaches, such as correlations of atomic displacements or dihedral angles, as well as a novel approach based on the correlation of Kabsch-Sander electrostatic couplings. MDiGest allows for comparisons of networks and community structures that capture physical information relevant to allostery. Multiple complementary tools for studying essential dynamics include principal component analysis, root mean square fluctuation, as well as secondary structure-based analyses.
| S-EPMC10769569 | biostudies-literature
Project description:Lipids play important modulatory and structural roles for membrane proteins. Molecular dynamics simulations are frequently used to provide insights into the nature of these protein-lipid interactions. Systematic comparative analysis requires tools that provide algorithms for objective assessment of such interactions. We introduce PyLipID, a Python package for the identification and characterization of specific lipid interactions and binding sites on membrane proteins from molecular dynamics simulations. PyLipID uses a community analysis approach for binding site detection, calculating lipid residence times for both the individual protein residues and the detected binding sites. To assist structural analysis, PyLipID produces representative bound lipid poses from simulation data, using a density-based scoring function. To estimate residue contacts robustly, PyLipID uses a dual-cutoff scheme to differentiate between lipid conformational rearrangements while bound from full dissociation events. In addition to the characterization of protein-lipid interactions, PyLipID is applicable to analysis of the interactions of membrane proteins with other ligands. By combining automated analysis, efficient algorithms, and open-source distribution, PyLipID facilitates the systematic analysis of lipid interactions from large simulation data sets of multiple species of membrane proteins.
| S-EPMC8830038 | biostudies-literature
Project description:BackgroundEven though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.ResultsWe present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.ConclusionCosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
| S-EPMC7597035 | biostudies-literature
Project description:Vibrational motions in electronically excited states can be observed either by time and frequency resolved infrared absorption or by off resonant stimulated Raman techniques. Multipoint correlation function expressions are derived for both signals. Three representations which suggest different simulation protocols for the signals are developed. These are based on the forward and the backward propagation of the wavefunction, sum over state expansion using an effective vibrational Hamiltonian or a semiclassical treatment of a bath. We show that the effective temporal (Δt) and spectral (Δω) resolution of the techniques is not controlled solely by experimental knobs but also depends on the system dynamics being probed. The Fourier uncertainty ΔωΔt > 1 is never violated.
| S-EPMC3744248 | biostudies-literature
Project description:Regional connectivity-based parcellation (rCBP) is a widely used procedure for investigating the structural and functional differentiation within a region of interest (ROI) based on its long-range connectivity. No standardized software or guidelines currently exist for applying rCBP, making the method only accessible to those who develop their own tools. As such, there exists a discrepancy between the laboratories applying the procedure each with their own software solutions, making it difficult to compare and interpret the results. Here, we outline an rCBP procedure accompanied by an open source software package called CBPtools. CBPtools is a Python (version 3.5+) package that allows users to run an extensively evaluated rCBP analysis workflow on a given ROI. It currently supports two modalities: resting-state functional connectivity and structural connectivity based on diffusion-weighted imaging, along with support for custom connectivity matrices. Analysis parameters are customizable and the workflow can be scaled to a large number of subjects using a parallel processing environment. Parcellation results with corresponding validity metrics are provided as textual and graphical output. Thus, CBPtools provides a simple plug-and-play, yet customizable way to conduct rCBP analyses. By providing an open-source software we hope to promote reproducible and comparable rCBP analyses and, importantly, make the rCBP procedure readily available. Here, we demonstrate the utility of CBPtools using a voluminous data set on an average compute-cluster infrastructure by performing rCBP on three ROIs prominently featured in parcellation literature.
| S-EPMC7271019 | biostudies-literature
Project description:In the present work, we employ excited state accelerated ab initio molecular dynamics (A-AIMD) to efficiently study the excited state energy landscape and photophysical topology of a variety of molecular systems. In particular, we focus on two important challenges for the modeling of excited electronic states: (i) the identification and characterization of conical intersections and crossing seams, in order to predict different and often competing radiationless decay mechanisms, and (ii) the description of the solvent effect on the absorption and emission spectra of chemical species in solution. In particular, using as examples the Schiff bases formaldimine and salicylidenaniline, we show that A-AIMD can be readily employed to explore the conformational space around crossing seams in molecular systems with very different photochemistry. Using acetone in water as an example, we demonstrate that the enhanced configurational space sampling may be used to accurately and efficiently describe both the prominent features and line-shapes of absorption and emission spectra.
| S-EPMC3419466 | biostudies-other
Project description:The DynaSig-ML ('Dynamical Signatures-Machine Learning') Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. It does so by predicting 3D structural dynamics for every variant using the Elastic Network Contact Model (ENCoM), a sequence-sensitive coarse-grained normal mode analysis model. Dynamical Signatures represent the fluctuation at every position in the biomolecule and are used as features fed into machine learning models of the user's choice. Once trained, these models can be used to predict experimental outcomes for theoretical variants. The whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps are easily parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the maturation efficiency of human microRNA miR-125a variants from high-throughput enzymatic assays.Availability and implementationDynaSig-ML is open-source software available at https://github.com/gregorpatof/dynasigml_package.
| S-EPMC10130421 | biostudies-literature
Project description:We show that machine learning (ML) can be used to accurately reproduce nonadiabatic excited-state dynamics with decoherence-corrected fewest switches surface hopping in a 1-D model system. We propose to use ML to significantly reduce the simulation time of realistic, high-dimensional systems with good reproduction of observables obtained from reference simulations. Our approach is based on creating approximate ML potentials for each adiabatic state using a small number of training points. We investigate the feasibility of this approach by using adiabatic spin-boson Hamiltonian models of various dimensions as reference methods.
| S-EPMC6174422 | biostudies-literature
Project description:We describe the photophysical processes that give rise to thermally activated delayed fluorescence in the excited state intramolecular proton transfer (ESIPT) molecule, triquinolonobenzene (TQB). Using transient absorption and time-resolved photoluminescence spectroscopy, we fully characterize prompt and delayed emission, phosphorescence, and oxygen quenching to reveal the reverse intersystem crossing mechanism (rISC). After photoexcitation and rapid ESIPT to the TQB-TB tautomer, emission from S1 is found to compete with thermally activated ISC to an upper triplet state, T2, very close in energy to S1 and limiting photoluminescence quantum yield. T2 slowly decays to the lowest triplet state, T1, via internal conversion. In the presence of oxygen, T2 is quenched to the ground state of the double proton transferred TQB-TC tautomer. Our measurements demonstrate that rISC in TQB occurs from T2 to S1 driven by thermally activated reverse internal conversion from T1 to T2 and support recent calculations by Cao et al. (Cao, Y.; Eng, J.; Penfold, T. J. Excited State Intramolecular Proton Transfer Dynamics for Triplet Harvesting in Organic Molecules. J. Phys. Chem. A 2019, 123, 2640-2649).
| S-EPMC7304890 | biostudies-literature
Project description:Efficiency and performance of light triggered molecular motors are crucial features that need to be mechanistically understood to improve the performance and enable conscious property tailoring for specific applications. In this work, three different hemithioindigo-based molecular motors are investigated and all four steps in their complete unidirectional rotation are unraveled fully quantitatively. Transient absorption spectroscopy across twelve orders of magnitude in time is used to probe the fs nuclear motions up to the ms thermal kinetics, covering the timeframe of the whole motor rotation. The newly known full mechanisms allow simulation of the motor systems to scrutinize their performance at realistic illumination conditions. This highlights the importance of photoisomerization quantum yields for the rotation speed. The substitution pattern in close proximity to the rotation axle influences the excited and ground state properties. Reduction of electron donation and concomitant increase of steric hindrance leads to faster photoisomerization reactions with quasi-ballistic behavior, but also to a slight decrease in the quantum efficiency. The expected decelerating effects of increased sterics are primarily manifested in the ground state. A promising approach for next-generation hemithioindigo motors is to elevate electron donation at the rotor fragment followed by an increase of steric hindrance.
| S-EPMC7702134 | biostudies-literature